---
base_model: Snowflake/snowflake-arctic-embed-m
library_name: sentence-transformers
metrics:
- cosine_accuracy@1
- cosine_accuracy@3
- cosine_accuracy@5
- cosine_accuracy@10
- cosine_precision@1
- cosine_precision@3
- cosine_precision@5
- cosine_precision@10
- cosine_recall@1
- cosine_recall@3
- cosine_recall@5
- cosine_recall@10
- cosine_ndcg@10
- cosine_mrr@10
- cosine_map@100
- dot_accuracy@1
- dot_accuracy@3
- dot_accuracy@5
- dot_accuracy@10
- dot_precision@1
- dot_precision@3
- dot_precision@5
- dot_precision@10
- dot_recall@1
- dot_recall@3
- dot_recall@5
- dot_recall@10
- dot_ndcg@10
- dot_mrr@10
- dot_map@100
pipeline_tag: sentence-similarity
tags:
- sentence-transformers
- sentence-similarity
- feature-extraction
- generated_from_trainer
- dataset_size:600
- loss:MatryoshkaLoss
- loss:MultipleNegativesRankingLoss
widget:
- source_sentence: What considerations should be taken into account regarding the
specific set or types of users for the AI system?
sentences:
- "46 \nMG-4.3-003 \nReport GAI incidents in compliance with legal and regulatory\
\ requirements (e.g., \nHIPAA breach reporting, e.g., OCR (2023) or NHTSA (2022)\
\ autonomous vehicle \ncrash reporting requirements. \nInformation Security; Data\
\ Privacy \nAI Actor Tasks: AI Deployment, Affected Individuals and Communities,\
\ Domain Experts, End-Users, Human Factors, Operation and \nMonitoring"
- "reporting, data protection, data privacy, or other laws. \nData Privacy; Human-AI\
\ \nConfiguration; Information \nSecurity; Value Chain and \nComponent Integration;\
\ Harmful \nBias and Homogenization \nGV-6.2-004 \nEstablish policies and procedures\
\ for continuous monitoring of third-party GAI \nsystems in deployment. \nValue\
\ Chain and Component \nIntegration \nGV-6.2-005 \nEstablish policies and procedures\
\ that address GAI data redundancy, including \nmodel weights and other system\
\ artifacts."
- "times, and availability of critical support. \nHuman-AI Configuration; \nInformation\
\ Security; Value Chain \nand Component Integration \nAI Actor Tasks: AI Deployment,\
\ Operation and Monitoring, TEVV, Third-party entities \n \nMAP 1.1: Intended\
\ purposes, potentially beneficial uses, context specific laws, norms and expectations,\
\ and prospective settings in \nwhich the AI system will be deployed are understood\
\ and documented. Considerations include: the specific set or types of users"
- source_sentence: What should organizations leverage when deploying GAI applications
and using third-party pre-trained models?
sentences:
- "external use, narrow vs. broad application scope, fine-tuning, and varieties of\
\ \ndata sources (e.g., grounding, retrieval-augmented generation). \nData Privacy;\
\ Intellectual \nProperty"
- "44 \nMG-3.2-007 \nLeverage feedback and recommendations from organizational boards\
\ or \ncommittees related to the deployment of GAI applications and content \n\
provenance when using third-party pre-trained models. \nInformation Integrity;\
\ Value Chain \nand Component Integration \nMG-3.2-008 \nUse human moderation\
\ systems where appropriate to review generated content \nin accordance with human-AI\
\ configuration policies established in the Govern"
- "Security \nMS-2.7-003 \nConduct user surveys to gather user satisfaction with\
\ the AI-generated content \nand user perceptions of content authenticity. Analyze\
\ user feedback to identify \nconcerns and/or current literacy levels related\
\ to content provenance and \nunderstanding of labels on content. \nHuman-AI Configuration;\
\ \nInformation Integrity \nMS-2.7-004 \nIdentify metrics that reflect the effectiveness\
\ of security measures, such as data"
- source_sentence: What are the potential positive and negative impacts of AI system
uses on individuals and communities?
sentences:
- "and Homogenization \nAI Actor Tasks: AI Deployment, Affected Individuals and Communities,\
\ End-Users, Operation and Monitoring, TEVV \n \nMEASURE 4.2: Measurement results\
\ regarding AI system trustworthiness in deployment context(s) and across the\
\ AI lifecycle are \ninformed by input from domain experts and relevant AI Actors\
\ to validate whether the system is performing consistently as \nintended. Results\
\ are documented. \nAction ID \nSuggested Action \nGAI Risks \nMS-4.2-001"
- "bias based on race, gender, disability, or other protected classes. \nHarmful\
\ bias in GAI systems can also lead to harms via disparities between how a model\
\ performs for \ndifferent subgroups or languages (e.g., an LLM may perform less\
\ well for non-English languages or \ncertain dialects). Such disparities can\
\ contribute to discriminatory decision-making or amplification of \nexisting societal\
\ biases. In addition, GAI systems may be inappropriately trusted to perform similarly"
- "along with their expectations; potential positive and negative impacts of system\
\ uses to individuals, communities, organizations, \nsociety, and the planet;\
\ assumptions and related limitations about AI system purposes, uses, and risks\
\ across the development or \nproduct AI lifecycle; and related TEVV and system\
\ metrics. \nAction ID \nSuggested Action \nGAI Risks \nMP-1.1-001 \nWhen identifying\
\ intended purposes, consider factors such as internal vs."
- source_sentence: How does the suggested action MG-41-001 aim to address GAI risks?
sentences:
- "most appropriate baseline is to compare against, which can result in divergent\
\ views on when a disparity between \nAI behaviors for different subgroups constitutes\
\ a harm. In discussing harms from disparities such as biased \nbehavior, this\
\ document highlights examples where someone’s situation is worsened relative\
\ to what it would have \nbeen in the absence of any AI system, making the outcome\
\ unambiguously a harm of the system."
- "Harmful Bias Managed, Privacy Enhanced, Safe, Secure and Resilient, Valid and\
\ Reliable \n3. \nSuggested Actions to Manage GAI Risks \nThe following suggested\
\ actions target risks unique to or exacerbated by GAI. \nIn addition to the suggested\
\ actions below, AI risk management activities and actions set forth in the AI\
\ \nRMF 1.0 and Playbook are already applicable for managing GAI risks. Organizations\
\ are encouraged to"
- "MANAGE 4.1: Post-deployment AI system monitoring plans are implemented, including\
\ mechanisms for capturing and evaluating \ninput from users and other relevant\
\ AI Actors, appeal and override, decommissioning, incident response, recovery,\
\ and change \nmanagement. \nAction ID \nSuggested Action \nGAI Risks \nMG-4.1-001\
\ \nCollaborate with external researchers, industry experts, and community \n\
representatives to maintain awareness of emerging best practices and"
- source_sentence: What are some examples of input data features that may serve as
proxies for demographic group membership in GAI systems?
sentences:
- "data privacy violations, obscenity, extremism, violence, or CBRN information\
\ in \nsystem training data. \nData Privacy; Intellectual Property; \nObscene,\
\ Degrading, and/or \nAbusive Content; Harmful Bias and \nHomogenization; Dangerous,\
\ \nViolent, or Hateful Content; CBRN \nInformation or Capabilities \nMS-2.6-003\
\ Re-evaluate safety features of fine-tuned models when the negative risk exceeds\
\ \norganizational risk tolerance. \nDangerous, Violent, or Hateful \nContent"
- "GAI. \nInformation Integrity; Intellectual \nProperty \nAI Actor Tasks: Governance\
\ and Oversight, Operation and Monitoring \n \nGOVERN 1.6: Mechanisms are in place\
\ to inventory AI systems and are resourced according to organizational risk priorities.\
\ \nAction ID \nSuggested Action \nGAI Risks \nGV-1.6-001 Enumerate organizational\
\ GAI systems for incorporation into AI system inventory \nand adjust AI system\
\ inventory requirements to account for GAI risks. \nInformation Security"
- "complex or unstructured data; Input data features that may serve as proxies for\
\ \ndemographic group membership (i.e., image metadata, language dialect) or \n\
otherwise give rise to emergent bias within GAI systems; The extent to which \n\
the digital divide may negatively impact representativeness in GAI system \ntraining\
\ and TEVV data; Filtering of hate speech or content in GAI system \ntraining\
\ data; Prevalence of GAI-generated data in GAI system training data. \nHarmful\
\ Bias and Homogenization"
model-index:
- name: SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
results:
- task:
type: information-retrieval
name: Information Retrieval
dataset:
name: Unknown
type: unknown
metrics:
- type: cosine_accuracy@1
value: 0.85
name: Cosine Accuracy@1
- type: cosine_accuracy@3
value: 0.975
name: Cosine Accuracy@3
- type: cosine_accuracy@5
value: 1.0
name: Cosine Accuracy@5
- type: cosine_accuracy@10
value: 1.0
name: Cosine Accuracy@10
- type: cosine_precision@1
value: 0.85
name: Cosine Precision@1
- type: cosine_precision@3
value: 0.325
name: Cosine Precision@3
- type: cosine_precision@5
value: 0.19999999999999998
name: Cosine Precision@5
- type: cosine_precision@10
value: 0.09999999999999999
name: Cosine Precision@10
- type: cosine_recall@1
value: 0.85
name: Cosine Recall@1
- type: cosine_recall@3
value: 0.975
name: Cosine Recall@3
- type: cosine_recall@5
value: 1.0
name: Cosine Recall@5
- type: cosine_recall@10
value: 1.0
name: Cosine Recall@10
- type: cosine_ndcg@10
value: 0.9341754705038519
name: Cosine Ndcg@10
- type: cosine_mrr@10
value: 0.911875
name: Cosine Mrr@10
- type: cosine_map@100
value: 0.9118749999999999
name: Cosine Map@100
- type: dot_accuracy@1
value: 0.85
name: Dot Accuracy@1
- type: dot_accuracy@3
value: 0.975
name: Dot Accuracy@3
- type: dot_accuracy@5
value: 1.0
name: Dot Accuracy@5
- type: dot_accuracy@10
value: 1.0
name: Dot Accuracy@10
- type: dot_precision@1
value: 0.85
name: Dot Precision@1
- type: dot_precision@3
value: 0.325
name: Dot Precision@3
- type: dot_precision@5
value: 0.19999999999999998
name: Dot Precision@5
- type: dot_precision@10
value: 0.09999999999999999
name: Dot Precision@10
- type: dot_recall@1
value: 0.85
name: Dot Recall@1
- type: dot_recall@3
value: 0.975
name: Dot Recall@3
- type: dot_recall@5
value: 1.0
name: Dot Recall@5
- type: dot_recall@10
value: 1.0
name: Dot Recall@10
- type: dot_ndcg@10
value: 0.9341754705038519
name: Dot Ndcg@10
- type: dot_mrr@10
value: 0.911875
name: Dot Mrr@10
- type: dot_map@100
value: 0.9118749999999999
name: Dot Map@100
---
# SentenceTransformer based on Snowflake/snowflake-arctic-embed-m
This is a [sentence-transformers](https://www.SBERT.net) model finetuned from [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m). It maps sentences & paragraphs to a 768-dimensional dense vector space and can be used for semantic textual similarity, semantic search, paraphrase mining, text classification, clustering, and more.
## Model Details
### Model Description
- **Model Type:** Sentence Transformer
- **Base model:** [Snowflake/snowflake-arctic-embed-m](https://huggingface.co/Snowflake/snowflake-arctic-embed-m)
- **Maximum Sequence Length:** 512 tokens
- **Output Dimensionality:** 768 tokens
- **Similarity Function:** Cosine Similarity
### Model Sources
- **Documentation:** [Sentence Transformers Documentation](https://sbert.net)
- **Repository:** [Sentence Transformers on GitHub](https://github.com/UKPLab/sentence-transformers)
- **Hugging Face:** [Sentence Transformers on Hugging Face](https://huggingface.co/models?library=sentence-transformers)
### Full Model Architecture
```
SentenceTransformer(
(0): Transformer({'max_seq_length': 512, 'do_lower_case': False}) with Transformer model: BertModel
(1): Pooling({'word_embedding_dimension': 768, 'pooling_mode_cls_token': True, 'pooling_mode_mean_tokens': False, 'pooling_mode_max_tokens': False, 'pooling_mode_mean_sqrt_len_tokens': False, 'pooling_mode_weightedmean_tokens': False, 'pooling_mode_lasttoken': False, 'include_prompt': True})
(2): Normalize()
)
```
## Usage
### Direct Usage (Sentence Transformers)
First install the Sentence Transformers library:
```bash
pip install -U sentence-transformers
```
Then you can load this model and run inference.
```python
from sentence_transformers import SentenceTransformer
# Download from the 🤗 Hub
model = SentenceTransformer("sentence_transformers_model_id")
# Run inference
sentences = [
'What are some examples of input data features that may serve as proxies for demographic group membership in GAI systems?',
'complex or unstructured data; Input data features that may serve as proxies for \ndemographic group membership (i.e., image metadata, language dialect) or \notherwise give rise to emergent bias within GAI systems; The extent to which \nthe digital divide may negatively impact representativeness in GAI system \ntraining and TEVV data; Filtering of hate speech or content in GAI system \ntraining data; Prevalence of GAI-generated data in GAI system training data. \nHarmful Bias and Homogenization',
'GAI. \nInformation Integrity; Intellectual \nProperty \nAI Actor Tasks: Governance and Oversight, Operation and Monitoring \n \nGOVERN 1.6: Mechanisms are in place to inventory AI systems and are resourced according to organizational risk priorities. \nAction ID \nSuggested Action \nGAI Risks \nGV-1.6-001 Enumerate organizational GAI systems for incorporation into AI system inventory \nand adjust AI system inventory requirements to account for GAI risks. \nInformation Security',
]
embeddings = model.encode(sentences)
print(embeddings.shape)
# [3, 768]
# Get the similarity scores for the embeddings
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [3, 3]
```
## Evaluation
### Metrics
#### Information Retrieval
* Evaluated with [InformationRetrievalEvaluator](https://sbert.net/docs/package_reference/sentence_transformer/evaluation.html#sentence_transformers.evaluation.InformationRetrievalEvaluator)
| Metric | Value |
|:--------------------|:-----------|
| cosine_accuracy@1 | 0.85 |
| cosine_accuracy@3 | 0.975 |
| cosine_accuracy@5 | 1.0 |
| cosine_accuracy@10 | 1.0 |
| cosine_precision@1 | 0.85 |
| cosine_precision@3 | 0.325 |
| cosine_precision@5 | 0.2 |
| cosine_precision@10 | 0.1 |
| cosine_recall@1 | 0.85 |
| cosine_recall@3 | 0.975 |
| cosine_recall@5 | 1.0 |
| cosine_recall@10 | 1.0 |
| cosine_ndcg@10 | 0.9342 |
| cosine_mrr@10 | 0.9119 |
| **cosine_map@100** | **0.9119** |
| dot_accuracy@1 | 0.85 |
| dot_accuracy@3 | 0.975 |
| dot_accuracy@5 | 1.0 |
| dot_accuracy@10 | 1.0 |
| dot_precision@1 | 0.85 |
| dot_precision@3 | 0.325 |
| dot_precision@5 | 0.2 |
| dot_precision@10 | 0.1 |
| dot_recall@1 | 0.85 |
| dot_recall@3 | 0.975 |
| dot_recall@5 | 1.0 |
| dot_recall@10 | 1.0 |
| dot_ndcg@10 | 0.9342 |
| dot_mrr@10 | 0.9119 |
| dot_map@100 | 0.9119 |
## Training Details
### Training Dataset
#### Unnamed Dataset
* Size: 600 training samples
* Columns: sentence_0 and sentence_1
* Approximate statistics based on the first 600 samples:
| | sentence_0 | sentence_1 |
|:--------|:-----------------------------------------------------------------------------------|:-----------------------------------------------------------------------------------|
| type | string | string |
| details | - min: 11 tokens
- mean: 20.85 tokens
- max: 35 tokens
| - min: 8 tokens
- mean: 89.39 tokens
- max: 335 tokens
|
* Samples:
| sentence_0 | sentence_1 |
|:-------------------------------------------------------------------------------------------------------------------|:-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| What is the title of the publication related to Artificial Intelligence Risk Management by NIST? | NIST Trustworthy and Responsible AI
NIST AI 600-1
Artificial Intelligence Risk Management
Framework: Generative Artificial
Intelligence Profile
This publication is available free of charge from:
https://doi.org/10.6028/NIST.AI.600-1 |
| Where can the NIST AI 600-1 publication be accessed for free? | NIST Trustworthy and Responsible AI
NIST AI 600-1
Artificial Intelligence Risk Management
Framework: Generative Artificial
Intelligence Profile
This publication is available free of charge from:
https://doi.org/10.6028/NIST.AI.600-1 |
| What is the title of the publication released by NIST in July 2024 regarding artificial intelligence? | NIST Trustworthy and Responsible AI
NIST AI 600-1
Artificial Intelligence Risk Management
Framework: Generative Artificial
Intelligence Profile
This publication is available free of charge from:
https://doi.org/10.6028/NIST.AI.600-1
July 2024
U.S. Department of Commerce
Gina M. Raimondo, Secretary
National Institute of Standards and Technology
Laurie E. Locascio, NIST Director and Under Secretary of Commerce for Standards and Technology |
* Loss: [MatryoshkaLoss](https://sbert.net/docs/package_reference/sentence_transformer/losses.html#matryoshkaloss) with these parameters:
```json
{
"loss": "MultipleNegativesRankingLoss",
"matryoshka_dims": [
768,
512,
256,
128,
64
],
"matryoshka_weights": [
1,
1,
1,
1,
1
],
"n_dims_per_step": -1
}
```
### Training Hyperparameters
#### Non-Default Hyperparameters
- `eval_strategy`: steps
- `per_device_train_batch_size`: 20
- `per_device_eval_batch_size`: 20
- `num_train_epochs`: 5
- `multi_dataset_batch_sampler`: round_robin
#### All Hyperparameters
Click to expand
- `overwrite_output_dir`: False
- `do_predict`: False
- `eval_strategy`: steps
- `prediction_loss_only`: True
- `per_device_train_batch_size`: 20
- `per_device_eval_batch_size`: 20
- `per_gpu_train_batch_size`: None
- `per_gpu_eval_batch_size`: None
- `gradient_accumulation_steps`: 1
- `eval_accumulation_steps`: None
- `torch_empty_cache_steps`: None
- `learning_rate`: 5e-05
- `weight_decay`: 0.0
- `adam_beta1`: 0.9
- `adam_beta2`: 0.999
- `adam_epsilon`: 1e-08
- `max_grad_norm`: 1
- `num_train_epochs`: 5
- `max_steps`: -1
- `lr_scheduler_type`: linear
- `lr_scheduler_kwargs`: {}
- `warmup_ratio`: 0.0
- `warmup_steps`: 0
- `log_level`: passive
- `log_level_replica`: warning
- `log_on_each_node`: True
- `logging_nan_inf_filter`: True
- `save_safetensors`: True
- `save_on_each_node`: False
- `save_only_model`: False
- `restore_callback_states_from_checkpoint`: False
- `no_cuda`: False
- `use_cpu`: False
- `use_mps_device`: False
- `seed`: 42
- `data_seed`: None
- `jit_mode_eval`: False
- `use_ipex`: False
- `bf16`: False
- `fp16`: False
- `fp16_opt_level`: O1
- `half_precision_backend`: auto
- `bf16_full_eval`: False
- `fp16_full_eval`: False
- `tf32`: None
- `local_rank`: 0
- `ddp_backend`: None
- `tpu_num_cores`: None
- `tpu_metrics_debug`: False
- `debug`: []
- `dataloader_drop_last`: False
- `dataloader_num_workers`: 0
- `dataloader_prefetch_factor`: None
- `past_index`: -1
- `disable_tqdm`: False
- `remove_unused_columns`: True
- `label_names`: None
- `load_best_model_at_end`: False
- `ignore_data_skip`: False
- `fsdp`: []
- `fsdp_min_num_params`: 0
- `fsdp_config`: {'min_num_params': 0, 'xla': False, 'xla_fsdp_v2': False, 'xla_fsdp_grad_ckpt': False}
- `fsdp_transformer_layer_cls_to_wrap`: None
- `accelerator_config`: {'split_batches': False, 'dispatch_batches': None, 'even_batches': True, 'use_seedable_sampler': True, 'non_blocking': False, 'gradient_accumulation_kwargs': None}
- `deepspeed`: None
- `label_smoothing_factor`: 0.0
- `optim`: adamw_torch
- `optim_args`: None
- `adafactor`: False
- `group_by_length`: False
- `length_column_name`: length
- `ddp_find_unused_parameters`: None
- `ddp_bucket_cap_mb`: None
- `ddp_broadcast_buffers`: False
- `dataloader_pin_memory`: True
- `dataloader_persistent_workers`: False
- `skip_memory_metrics`: True
- `use_legacy_prediction_loop`: False
- `push_to_hub`: False
- `resume_from_checkpoint`: None
- `hub_model_id`: None
- `hub_strategy`: every_save
- `hub_private_repo`: False
- `hub_always_push`: False
- `gradient_checkpointing`: False
- `gradient_checkpointing_kwargs`: None
- `include_inputs_for_metrics`: False
- `eval_do_concat_batches`: True
- `fp16_backend`: auto
- `push_to_hub_model_id`: None
- `push_to_hub_organization`: None
- `mp_parameters`:
- `auto_find_batch_size`: False
- `full_determinism`: False
- `torchdynamo`: None
- `ray_scope`: last
- `ddp_timeout`: 1800
- `torch_compile`: False
- `torch_compile_backend`: None
- `torch_compile_mode`: None
- `dispatch_batches`: None
- `split_batches`: None
- `include_tokens_per_second`: False
- `include_num_input_tokens_seen`: False
- `neftune_noise_alpha`: None
- `optim_target_modules`: None
- `batch_eval_metrics`: False
- `eval_on_start`: False
- `eval_use_gather_object`: False
- `batch_sampler`: batch_sampler
- `multi_dataset_batch_sampler`: round_robin